Inferring target clusters based on social connections

ABSTRACT

A seed cluster comprising a group of users who share a particular attribute and/or affiliation is determined by a social networking system. For each user of the seed cluster, other users and/or entities connected to the user in the social networking system are retrieved. For each retrieved other user or entity, the social networking system may determine whether the other user or entity exhibits the attribute or affiliation based on a random walk algorithm. A resulting targeting cluster of users and/or entities may be used for targeting advertisements targeting to members. A social networking system may also infer an affiliation for a user based on the user&#39;s interaction with a page, application, or entity where other users who interacted with the same page, application, or entity have the same affiliation.

RELATED APPLICATIONS

This application is a Divisional of U.S. patent application Ser. No.13/625,633, filed Sep. 24, 2012, which is incorporated by reference inits entirety.

BACKGROUND

This invention relates generally to social networking, and in particularto inferring target clusters of users based on social connections in asocial networking system.

Traditional targeting criteria for advertising relies on demographicdata and structured information, such as a user's self-declaredinterests and intentions to be marketable, i.e., to be in the market topurchase a product or service. Advertisers, in an effort to locate andtarget these users purchase analytical data gathered by third partiesthat track users visiting websites related to the advertiser's product.For example, websites on the Internet track people comparing car pricesand filling out a form for a test drive at a local dealership and sellthis information to advertisers. Advertisers may also target specifictypes of publishers or pages within a publisher's network in an effortto reach their intended audience (e.g., ads on Cars.com or the carscategory on Yahoo to reach users who are believed to be in the market tobuy a car.) But in the end, advertisers are limited to educated guessingat a user's intent to purchase or a user's interest in a particularsubject matter.

In recent years, users of social networking systems have shared theirinterests, attributes, and affiliations, engaging with other users ofthe social networking systems by sharing photos, real-time statusupdates, and playing social games. The amount of information gatheredfrom users is staggering—information describing recent moves to a newcity, political preferences, causes, graduations, births, engagements,marriages, and the like. Entities may also declare attributes,affiliations, and other information, such as pages representingbusinesses, groups of users representing various organizations, andapplications operating on social networking systems on behalf an entity.Users may also interact with these entities, providing more informationabout the users based on their interactions on the social networkingsystems. Social networking systems have been passively recording thisinformation as part of the user experience, but social networkingsystems have lacked robust tools to use this information about users fortargeting advertisements.

Specifically, the information available on social networking systems hasnot been used to define clusters of users of a social networking systemthat exhibit a particular attribute or have a particular affiliation,such as political party preference or interest in a niche topic such as“hipsters.” Social networking systems have not provided advertisers withtargeting clusters that utilize the information available about theusers of the social networking systems.

SUMMARY

A seed cluster comprising a group of users who share a particularattribute and/or affiliation is determined by a social networkingsystem. For each user of the seed cluster, a plurality of other usersand/or entities connected to the user in the social networking systemare retrieved. For each retrieved other user or entity, the socialnetworking system may determine whether the other user or entityexhibits the attribute or affiliation. An explicit declaration of theother user or entity, an analysis of the connected users and/or entitiesof the retrieved other user or entity, and/or a random walk algorithmmay be used to make this determination. As a result, the socialnetworking system may determine one or more inferences that other usersand/or entities exhibit the attribute or affiliation. The resultingtargeting cluster of users and/or entities may be used for targetingadvertisements targeting to members. The targeting cluster may be testedfor accuracy using performance testing (e.g., measuring click-throughrates of members in the targeting cluster for a particular advertisementand measuring negative feedback rates of members in the targetingcluster). A social networking system may also infer an affiliation for auser based on the user's interaction with a page, application, or entitywhere other users who interacted with the same page, application, orentity have the same affiliation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is high level block diagram illustrating a process of inferringaffiliations and/or attributes of users by analyzing their socialconnections in a social networking system, in accordance with anembodiment of the invention.

FIG. 2 is a network diagram of a system for inferring affiliationsand/or attributes of users by analyzing their social connections in asocial networking system, showing a block diagram of the socialnetworking system, in accordance with an embodiment of the invention.

FIG. 3 is high level block diagram illustrating an inferential targetingcluster module that includes various modules for analyzing socialconnections in a social networking system to infer affiliations ofusers, in accordance with an embodiment of the invention.

FIG. 4 is a flowchart diagram depicting a process of inferringaffiliations and/or attributes of users by analyzing social connectionsin a social networking system, in accordance with an embodiment of theinvention.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION Overview

A social networking system offers its users the ability to communicateand interact with other users of the social networking system. Usersjoin the social networking system and add connections to a number ofother users to whom they desire to be connected. Users of socialnetworking system can provide information describing them which isstored as user profiles. For example, users can provide their age,gender, geographical location, education history, employment history andthe like. The information provided by users may be used by the socialnetworking system to direct information to the user. For example, thesocial networking system may recommend social groups, events, andpotential friends to a user. The social networking system may alsoutilize user profile information to direct advertisements to the user,ensuring that only relevant advertisements are directed to the user.Relevant advertisements ensure that advertising spending reaches theirintended audiences, rather than wasting shrinking resources on usersthat are likely to ignore the advertisement.

In addition to declarative information provided by users, socialnetworking systems may also record users' actions on the socialnetworking system. These actions include communications with otherusers, sharing photos, interactions with applications that operate onthe social networking system, such as a social gaming application,responding to a poll, adding an interest, and joining an employeenetwork. Information about users, such as stronger interests inparticular users and applications than others based on their behavior,can be generated from these recorded actions through analysis andmachine learning by the social networking system.

A social networking system may also attempt to infer information aboutits users. A social networking system may analyze large bursts ofcomments on a user's wall or status update from other users that includekeywords such as “Congratulations” and “baby.” Though largelyunstructured, this information can be analyzed to infer life events thatare happening to users on the social networking system.

Further, user profile information for a user is often not complete andmay not even be completely accurate. Sometimes users deliberatelyprovide incorrect information; for example, a user may provide incorrectage in the user profile. Users may also forget to update theirinformation when it changes. For example, a user may move to a newlocation and forget to update the user's geographical location, or auser may change jobs but forget to update their workplace description inthe user profile. As a result, a social networking system may infercertain profile attributes of a user, such as geographic location,educational institutions attended, and age range, by analyzing theuser's connections and their declared profile information. Inferringprofile attributes are further discussed in a related application,“Inferring User Profile Attributes from Social Information,” U.S.application Ser. No. 12/916,322, filed Oct. 29, 2010, which isincorporated by reference in its entirety.

Reliable information about affiliations and attributes of users is veryvaluable to advertisers because users are more influenced by targetedadvertisements that are relevant to their affiliations and attributes.For example, users who, politically, declare themselves as liberal aremore susceptible to clicking on an advertisement for a petition forliberal causes. Meanwhile, advertisements that are not targeted based onattributes of users may have low click-through rates (CTRs) and/orconversions of advertisements provided to those users. Incorrectlytargeted advertisements, which may include advertisements that arerelevant to a particular attribute or affiliation but are not targetedto users that exhibit that attribute or affiliation, results in wastedadvertising spend due to the ineffective advertising. However, someusers may not declare their attributes and affiliations as part of theiruser profiles on a social networking system. As a result, targetingclusters based on these attributes and affiliations may be limited insize. A social networking system may infer affiliations and/orattributes of users by analyzing their social connections in the socialnetworking system. Inferred affiliations and/or attributes may then betested based on performance testing of advertisements targeted to theusers based on the inferred affiliations and/or attributes.

FIG. 1 illustrates a high-level block diagram of a process for inferringaffiliations and/or attributes of users by analyzing their socialconnections in a social networking system, in one embodiment. A socialnetworking system 100 may identify a seed cluster of users that haveself-declared a particular attribute or affiliation. Included in theseed cluster is a seed user 102 that is connected to friends 104 in thesocial networking system 100, or other users of the social networkingsystem 100. Each user of the social networking system 100 is associatedwith a user profile that includes declarative information shared by theuser as well as any attributes and/or affiliations inferred by thesocial networking system 100. In one embodiment, explicit targetingclusters based on one or more attributes of users of the socialnetworking system may be used as seed clusters. Methods of explicitlydefining targeting clusters that have been tested for accuracy isfurther discussed in a related application, “Defining and Checking theAccuracy of Explicit Target Clusters in a Social Networking System,”U.S. patent application Ser. No. 12/980,176, filed on Dec. 28, 2010,hereby incorporated by reference.

FIG. 1 and the other figures use like reference numerals to identifylike elements. A letter after a reference numeral, such as “104 a,”indicates that the text refers specifically to the element having thatparticular reference numeral. A reference numeral in the text without afollowing letter, such as “104,” refers to any or all of the elements inthe figures bearing that reference numeral (e.g. “104” in the textrefers to reference numerals “104 a,” “104 b,” and/or “104 c” in thefigures). Only five friends 104 of the seed user 102 are shown in FIG. 1in order to simplify and clarify the description.

In one embodiment, all friends 104 of the seed user 102 in the socialnetworking system 100, or other users connected to the seed user 102,are identified and analyzed by the social networking system 100 todetermine a user affiliation 106 for each friend 104. In anotherembodiment, a subset of the connections of a seed user 102 on the socialnetworking system 100 may be selected using a random walk method. Thesocial networking system 100 may analyze the subset of the connectionsof a seed user 102 to determine unknown user affiliations 106 of thefriends 104. A user affiliation 106 may be determined, as mentionedabove, by an explicit declaration in a user profile, such as a useraffiliation 106 f of the seed user 102 that she is a Democrat. Useraffiliations 106 also may have been inferred by the social networkingsystem 100. Some user affiliations 106 may not be known by the socialnetworking system 100 as a result of the associated users not providingthat information to the social networking system 100 in their userprofiles. The social networking system 100, as a result of not havinginformation about the user affiliations 106 of the friends 104, mayinfer those user affiliations 106 by analyzing other users connected tothe friends 104.

For example, as illustrated in FIG. 1, a friend 104 d connected to theseed user 102 and a friend 104 e connected to the seed user 102 in thesocial networking system 100 have unknown user affiliations 106 d and106 e. The social networking system 100 may have determined useraffiliations 106 a, 106 b, and 106 c for friends 104 a, 104 b, and 104 cby retrieving user profiles for the friends 104 a, 104 b, and 104 c onthe social networking system 100, in one embodiment. In anotherembodiment, a user affiliation 106 a may have been determined by thesocial networking system 100 by inference after analyzing other usersconnected to the friend 104 a. User affiliation 106 d may be determinedby the social networking system 100 using similar methods.

As further illustrated in FIG. 1, the friend 104 d connected to the seeduser 102 is connected to other users and entities in the socialnetworking system 100 that may be analyzed to infer the user affiliation106 d. The friend 104 d is connected to friends of friend (“FOFs”) 112a, 112 b, and 112 c as well as a page 108. The FOFs 112 a and 112 b haveuser affiliations 114 a and 114 b of Democrat (“Dem.”) while the FOF 112c has a user affiliation 114 c of Republican (“Rep.”). The page 108 mayhave a page affiliation 110 determined by the social networking system100 by analyzing users and entities connected to the page 108 in thesocial networking system 100 as well as interactions generated on thepage 108 by the users and entities. Here, the page affiliation 110 ofthe page 108 is Democrat. In other embodiments, other entities,represented by applications, events, groups, and custom graph objects,connected to a seed user 102 and/or friends 104 in the social networkingsystem 100, may have affiliations associated with them that may be usedby the social networking system 100 in determining an inference.

The social networking system 100 may determine the user affiliation 106d of the friend 104 d by determining the user affiliations 114 of theFOFs 112 as well as any page affiliations 110 of pages 108 connected tothe friend 104 d in the social networking system that are relevant tothe determination of the user affiliation 106 d. In one embodiment, thesocial networking system 100 may infer that the user affiliation 106 dof the friend 104 d is Democrat based on a simple majority ofconnections in the social networking system 100 being Democrat. Inanother embodiment, a scoring model may be used to determine theinference using affinity scores for the FOFs 112 of the friend 104 d andthe affinity score for the page 108 of the friend 104 d as weights inthe scoring model. In one embodiment, a scoring model may be a weightedaverage. In another embodiment, a scoring model may include variousmethods for predicting confidence scores using machine learning and/orregression analysis. In a further embodiment, a random walk method maybe used to traverse a subset of all connections of a friend 104 d todetermine an inference of the user affiliation 106 d of the friend 104d. The random walk method may be useful in sampling a large number ofconnections to determine the inference, in one embodiment.

Another method of determining an inference of a user affiliation 106 efor a friend 104 e may include inferring a particular affiliation basedon a user's interaction with a page, application, or other entity in thesocial networking system 100 where other users that interacted with thepage, application, or other entity have the particular affiliation.Similar to determining a user affiliation 106 for a friend 104, thesocial networking system 100 may also determine a page affiliation 110for a page 108. A page 108 may represent an entity in the socialnetworking system 100, such as a celebrity entertainer like BritneySpears, a cause such as VH1 Save the Music, or a business such asPepsiCola. A social networking system 100 may be able to assign a pageaffiliation 110 for a page 108 based on the user affiliations of usersthat interact with the page 108. For example, if a majority of usersthat interact regularly with a page 108 have a particular useraffiliation, such as being a Democrat, then the page affiliation 110 maybe assigned that particular user affiliation, in one embodiment. Inother embodiments, other scoring models may be used in assigning theparticular user affiliation as the page affiliation 110 for a page 108,including weighted scoring models and prediction models that rely onmachine learning and regression analysis. Using the page affiliation 110for a page 108, the social networking system 100 may determineinferences of user affiliations for users that have interacted with thepage, in one embodiment. As a result of a friend 104 e interacting witha page 108 for Britney Spears that may have been assigned a pageaffiliation 110 of Democrat, the user affiliation 106 e for the friend104 e may be assigned Democrat, for example.

Other affiliations aside from political affiliations may be inferred bythe social networking system 100, such as user characteristics, topics,interests, professions, hobbies, sports fans, causes, music genrefanatics, and so on. For example, each user of a seed cluster of usersmay indicate an interest, or a “like” for one or more pages for variousmarathons throughout the year. This may indicate that these users in theseed cluster have a shared user characteristic of being “athletic.”Using the methods described above, the social networking system 100 maydetermine one or more inferences that other users connected to each userin the seed cluster may also share the user characteristic of being“athletic.” The inferred users may then be grouped into targetingclusters for advertisements related to the London Olympics. If aninferred user, when presented with a targeted advertisement based on theinferred user characteristic of being athletic, indicates that thetargeted advertisement is not relevant, not interesting, offensive, orotherwise provides negative feedback (e.g., by clicking on an ‘X’ in theadvertisement), that information may be used in modifying the scoringmodel used to determine the inference. Starting with an explicit groupof users, or seed cluster of users, the social networking system 100 mayexpand the group by inferring friends of members in the group using themethods described above.

Additionally, the social networking system 100 may infer an affiliationbased on a user's interaction with a page, application, and/or entitywhere other users who interacted with the same page, application, and/orentity have that affiliation. For example, a user may interactfrequently with a set of pages that may be related to being athletic,such as marathon pages, pages for sporting goods, and celebrity pagesfor sports figures. The level of interaction may vary, such as contentposts, comments, sharing content, “liking” content, and page views. Thesocial networking system 100 may determine that the majority of theusers that also interact with the same pages also exhibit the usercharacteristic, or have otherwise been affiliated through explicitaffiliation or inferred affiliation, of being athletic. As a result ofthe user's interactions with the same pages, the social networkingsystem 100 may infer that the user is also athletic. In anotherembodiment, a scoring model may be used to determine an affiliation of apage or entity on the social networking system 100 based on an analysisof the users that like the page. For example, a correlation may beidentified that links users that like country music, NASCAR racing,rodeos, and the Republican Party.

System Architecture

FIG. 2 is a high level block diagram illustrating a system environmentsuitable for inferring affiliations and/or attributes of users byanalyzing their social connections in a social networking system, inaccordance with an embodiment of the invention. The system environmentcomprises one or more user devices 202, the social networking system100, external websites 216, and a network 204. In alternativeconfigurations, different and/or additional modules can be included inthe system.

The user devices 202 comprise one or more computing devices that canreceive user input and can transmit and receive data via the network204. In one embodiment, the user device 202 is a conventional computersystem executing, for example, a Microsoft Windows-compatible operatingsystem (OS), Apple OS X, and/or a Linux distribution. In anotherembodiment, the user device 202 can be a device having computerfunctionality, such as a personal digital assistant (PDA), mobiletelephone, smart-phone, etc. The user device 202 is configured tocommunicate via network 204. The user device 202 can execute anapplication, for example, a browser application that allows a user ofthe user device 202 to interact with the social networking system 100.In another embodiment, the user device 202 interacts with the socialnetworking system 100 through an application programming interface (API)that runs on the native operating system of the user device 202, such asiOS and DROID.

In one embodiment, the network 204 uses standard communicationstechnologies and/or protocols. Thus, the network 204 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, digital subscriber line (DSL), etc.Similarly, the networking protocols used on the network 204 can includemultiprotocol label switching (MPLS), the transmission controlprotocol/Internet protocol (TCP/IP), the User Datagram Protocol (UDP),the hypertext transport protocol (HTTP), the simple mail transferprotocol (SMTP), and the file transfer protocol (FTP). The dataexchanged over the network 204 can be represented using technologiesand/or formats including the hypertext markup language (HTML) and theextensible markup language (XML). In addition, all or some of links canbe encrypted using conventional encryption technologies such as securesockets layer (SSL), transport layer security (TLS), and InternetProtocol security (IPsec).

FIG. 2 contains a block diagram of the social networking system 100. Thesocial networking system 100 includes a user profile store 206, a webserver 208, an action logger 210, a content store 212, an edge store214, an inferential targeting cluster module 218, and an advertisingtargeting module 220. In other embodiments, the social networking system100 may include additional, fewer, or different modules for variousapplications. Conventional components such as network interfaces,security functions, load balancers, failover servers, management andnetwork operations consoles, and the like are not shown so as to notobscure the details of the system.

The web server 208 links the social networking system 100 via thenetwork 204 to one or more user devices 202; the web server 208 servesweb pages, as well as other web-related content, such as Java, Flash,XML, and so forth. The web server 208 may provide the functionality ofreceiving and routing messages between the social networking system 100and the user devices 202, for example, instant messages, queued messages(e.g., email), text and SMS (short message service) messages, ormessages sent using any other suitable messaging technique. The user cansend a request to the web server 208 to upload information, for example,images or videos that are stored in the content store 212. Additionally,the web server 208 may provide API functionality to send data directlyto native client device operating systems, such as iOS, DROID, webOS,and RIM.

The action logger 210 is capable of receiving communications from theweb server 208 about user actions on and/or off the social networkingsystem 100. The action logger 210 populates an action log withinformation about user actions to track them. Such actions may include,for example, adding a connection to the other user, sending a message tothe other user, uploading an image, reading a message from the otheruser, viewing content associated with the other user, attending an eventposted by another user, among others. In addition, a number of actionsdescribed in connection with other objects are directed at particularusers, so these actions are associated with those users as well.

User account information and other related information for a user arestored in the user profile store 206. The user profile informationstored in user profile store 206 describes the users of the socialnetworking system 100, including biographic, demographic, and othertypes of descriptive information, such as work experience, educationalhistory, gender, hobbies or preferences, location, and the like. Theuser profile may also store other information provided by the user, forexample, images or videos. In certain embodiments, images of users maybe tagged with identification information of users of the socialnetworking system 100 displayed in an image. A user profile store 206maintains profile information about users of the social networkingsystem 100, such as age, gender, interests, geographic location, emailaddresses, credit card information, and other personalized information.The user profile store 206 also maintains references to the actionsstored in the action log and performed on objects in the content store212.

Although the system has access to the users' personal information,contained in the user profile store 206, the system preferably protectsthe users' information. For example, embodiments of the invention neverinclude any personally identifiable information with the clusters. Forexample, even if email addresses were stored in the user profile store206, the system may not build a cluster of users using their emailaddress. In one embodiment, the system may build a cluster of users whohave active credits tied to a credit card. Accordingly, while the systemwould avoid associating personally identifiable information with anindividual user, it may aggregate this information at the cluster level.

The edge store 214 stores the information describing the connectionsbetween users. The connections are defined by users, allowing users tospecify their relationships with other users. For example, theconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. In some embodiment, the connectionspecifies a connection type based on the type of relationship, forexample, family, or friend, or colleague. Users may select frompredefined types of connections, or define their own connection types asneeded. The edge store 214 acts as a cross-referencing database for theuser profile store 206 and the content store 212 to determine whichobjects are also being modified by connections of a user. Embodiments ofthe invention may also infer the relationship between two users (e.g.,using an affinity algorithm) and use that for cluster building (e.g., bybuilding a cluster of users whose close friends have upcoming birthdaysnext week, in which case the close friend would be identified using thecoefficient value).

An inferential targeting cluster module 218 may define seed clusters ofusers for inferring affiliations of other users of the social networkingsystem 100 connected to the users of the seed clusters. Informationretrieved from user profile objects stored in the user profile store206, content objects stored in the content store 212, and edge objectsstored in the edge store 214 may be used in determining seed clusters ofusers having a particular affiliation. The affiliation may be selectedby an advertiser in real time, through a targeting criteria selectionuser interface, or may be pre-selected by the social networking system100 as an optimization. The users that have inferred affiliations thenbecome part of the clusters of users to continue adding to the seedclusters. The inferential targeting cluster module 218 may also measurethe performance of these clusters by analyzing user actions onadvertisements that have been served to the clusters of users by the webserver 208. Negative feedback, such as an “X-out” action that indicatesthe advertisement was repetitive, irrelevant, offensive, or otherwiseobjectionable to the viewing user, as well as positive feedback in theform of clicking through the advertisements may be used in measuring theperformance of these clusters. User actions on the social networkingsystem 100 are recorded by the action logger 210.

An advertising targeting module 220 may receive targeting criteria foradvertisement requests from advertisers for targeting advertisements inthe social networking system. Using inferential targeting clustersgenerated by the inferential targeting cluster module 218, a targetedadvertisement on the social networking system 100 may be displayed on auser device 202 associated with a user of a targeting cluster of usersfor a particular affiliation, in one embodiment. Targeted advertisementsmay be distributed by the advertising targeting module 220 toinferential targeting clusters of users of the social networking system100, such as banner advertisements, social endorsements of anaffiliation or user characteristic, sponsored stories highlightinguser-generated stories related to an affiliation, and so forth.

Generating Inferential Targeting Clusters

FIG. 3 illustrates a high level block diagram of the inferentialtargeting cluster module 218 in further detail, in one embodiment. Theinferential targeting cluster module 218 includes a seed user selectionmodule 300, a user analysis module 302, an entity analysis module 304,an analytical scoring module 306, and a targeting cluster definitionmodule 308. These modules may perform in conjunction with each other orindependently to generate inferential targeting clusters foraffiliations, interests, and/or characteristics of users of the socialnetworking system 100.

A seed user selection module 300 gathers information about potentialseed users of the social networking system 100 to generate a seedcluster of users that share a selected affiliation, interest, orcharacteristic. In one embodiment, the seed user selection module 300selects users that have made the selected affiliation, interest, orcharacteristic explicit on their user profiles. For example, if theselected affiliation was a “San Francisco Giants Fan,” seed users may bedetermined based on an explicit affiliation by the seed users, such asbeing a “fan” of the San Francisco Giants page in the social networkingsystem 100, listing the sports team as a favorite sports team in theusers' profile page, and so forth. Other information may be gathered bythe seed user selection module 300, including edge objects and contentobjects related to the selected affiliation, interest, orcharacteristic. Continuing the example, content objects and edge objectsrelated to the selected affiliation, interest, and/or characteristic,such as check-in events, status update mentions, photo and video tags,likes on other pages related to the selected affiliation, and userinteractions with content items such as posts, comments, likes, andshares may be retrieved for determining whether a user may be selectedas a seed user in the seed cluster of users sharing the selectedaffiliation, interest, or characteristic.

A user analysis module 302 performs an analysis of other users connectedto a seed user in the seed cluster of users to identify additional usersthat may share the affiliation with the seed user. As illustrated inFIG. 1, other users connected to a seed user in the seed cluster may ormay not have the selected affiliation determined by the socialnetworking system 100. For those other users connected to the seed userthat do not have the selected affiliation determined, the user analysismodule 302 may perform an analysis on those other users to determinewhether they share the affiliation with the seed user. The user analysismodule 302 may determine that connected users to a seed user in the seedcluster of users have unknown affiliations. Further user analysis wouldbe required by the user analysis module 302 in that case, such asretrieving secondary connections, or users that are connected to theconnected users that have unknown affiliations. The retrieved secondaryconnections are then analyzed by the user analysis module 302 todetermine how many of those secondary connections share the affiliationwith the seed user. A user may be analyzed for the selected affiliationbased on the user's profile (i.e., self-declared) or may be inferred tohave the selected affiliation based on an analysis of edge objects andcontent objects related to the selected affiliation and the user, and/ora scoring model used to predict whether the user shares the selectedaffiliation.

An entity analysis module 304 determines an analysis of connections ofan entity, such as a page or application, on the social networkingsystem 100 with respect to a selected affiliation. For example, usersthat have explicitly associated themselves, or affiliated themselves,with the Republican Party may comprise the majority of users thatinteract with an application that enables users to find the closest LandRover dealership, which sells relatively large SUVs. The entity analysismodule 304 may analyze the connections of an entity, such as the usersthat have interacted with an application for finding the closest LandRover dealership, and determine what affiliations, if any, may beinferred about users that interact with the application. In this case,because a majority of users that use the Land Rover application areexplicitly affiliated with the Republican Party, the entity analysismodule 304 may determine that the application may have an affiliationwith the Republican Party such that users that interact with theapplication may be determined to also be affiliated with the RepublicanParty. In other embodiments, other types of interactions with entitiesor a series of disparate interactions may be analyzed by the entityanalysis module 304. For example, the entity analysis module 304 maylimit analysis of connected users to those users that have installed theapplication, have frequently used the application over a given timeperiod (by meeting a predetermined threshold number of uses), and haveinvited other users to install the application. In this way, the qualityof interaction with the entity may be used to identify a particularaffiliation that may be inferred to users that have performed thespecific type(s) of interactions with the entity.

An analytical scoring module 306 determines an analytical score foranalyzing connections of a user or a page in determining whether anaffiliation should be inferred for the user or the page. A scoringalgorithm or scoring model may be used by the analytical scoring module306 to determine an analytical score for a page or a user based on thepage's connections or the user's connections on the social networkingsystem 100. In one embodiment, the scoring model may determine whether amajority of the user's connections exhibit the affiliation explicitly orby inference. In another embodiment, other factors may be included inthe scoring model, such as using the affinity scores of the user for theuser's connections as weights in a weighted average of the user'sconnections exhibiting the affiliation. For example, a user that hasseveral weak connections on the social networking system 100 that haveself-identified as Republicans may have less weight attributed to themthan other connections that are self-identified as Democrats that havestronger connections to the user as reflected by higher affinity scores.

A targeting cluster definition module 308 generates a targeting clusterof users that share a particular affiliation. The targeting cluster maybe used for targeting advertisements, in one embodiment. In anotherembodiment, the targeting cluster may be used for various optimizationsin the social networking system 100, such as targeting content items ina news feed to users based on their affiliations. In a furtherembodiment, a targeting cluster definition module 308 may generate atargeting cluster of users that share one or more affiliations. Forexample, through analysis of affiliation information generated by theinferential targeting cluster module 218, a strong correlation may bedetermined for users affiliated with country music also being affiliatedwith the Republican Party. As a result, the targeting cluster definitionmodule 308 may determine that a target cluster definition for usersaffiliated with the Republican Party may include users that areaffiliated with country music. In other embodiments, only affiliationswith certain country music artists, such as Toby Keith and HankWilliams, may be strongly correlated with the being affiliated with theRepublican Party. Other country music artists, such as the Dixie Chicks,may be more affiliated with the Democrats, for example.

FIG. 4 illustrates a flow chart diagram depicting a process of inferringaffiliations and/or attributes of users and/or entities by analyzingsocial connections in a social networking system, in accordance with anembodiment of the invention. A selection of an attribute shared by asubset of users of a social networking system is received 402. Theattribute, or affiliation, may be selected by an advertiser selectingtargeting criteria for an ad request received 402 by the socialnetworking system 100, in one embodiment. In another embodiment, theattribute may be received 402 from another module in the socialnetworking system 100 for optimizing another social networking systemproduct, such as the news feed rankings of content items.

Once the selection of an attribute is received 402, a seed cluster isdetermined 404 as the subset of users sharing the selected attribute. Inone embodiment, the seed cluster is determined 404 by retrieving theuser profile objects of users that have explicitly stated the selectedattribute in their user profiles, or self-identified as having theselected attribute or affiliation. In another embodiment, a seed clustermay be determined 404 to include users that have been previouslyinferred to have the selected attribute or affiliation.

After the seed cluster has been determined 404 as the subset of userssharing the selected attribute, one or more primary connections of afirst user in the seed cluster are retrieved 406. The social networkingsystem 100 may use the seed cluster of users to identify primaryconnections that may have the selected attribute or affiliation. Here,primary connections may include users and non-users, such as entities,pages, and/or applications. Primary connections may be retrieved 406 byaccessing the first user's user profile object in the social networkingsystem 100, in one embodiment. In another embodiment, edge objectsassociated with the first user may be accessed to retrieve 406 theprimary connections of the first user in the social networking system100.

Next, one or more secondary connections of the first retrieved primaryconnection of the first user in the seed cluster is retrieved 408. Thefirst retrieved primary connection of the first user in the seed clustermay be a user or a non-user, such as an entity, a page, or anapplication. Secondary connections may also be users and/or non-users ofthe social networking system 100. Similar to step 406, secondaryconnections may be retrieved 408 by accessing the first retrievedprimary connection's user profile object, page object, or applicationobject to retrieve 408 the secondary connections or by accessing edgeobjects connected to the object representing the first retrieved primaryconnection in the social networking system 100.

Responsive to determining that the attribute is exhibited by a thresholdpercentage of the retrieved one or more secondary connections, thesocial networking system may declare 410 that the attribute is exhibitedby the first retrieved primary connection. The threshold percentage maybe predetermined, in one embodiment. In another embodiment, thethreshold percentage may be any percentage over 50%. In a furtherembodiment, the threshold percentage may be any percentage over 80%. Thesocial networking system 100 may declare 410 that the attribute isexhibited by the first retrieved primary connection by associating theattribute with a user profile object, a page object, or an applicationobject for the primary connection in the social networking system 100.

The determination that the attribute is exhibited by a thresholdpercentage of the retrieved one or more secondary connections may bemade responsive to applying a business logic rule to the one or moresecondary connections, in one embodiment. Other business logic rules maybe used to satisfy the determination that acts as a precondition to thesocial networking system declaring 410 that the attribute is exhibitedby the first retrieved primary connection. In one embodiment, a businesslogic rule may analyze a subset of the one or more secondary connectionsbased on affinity scores of the first retrieved primary connection forthe subset of the one or more secondary connections. The socialnetworking system may determine whether a majority that subset ofsecondary connections exhibit the attribute. In another embodiment, thesocial networking system may determine whether a predetermined thresholdpercentage of the subset of secondary connections exhibit the attribute.In other embodiments, different business logic rules may be used toestablish a precondition before declaring 410 that the attribute isexhibited by the first retrieved primary connection. For example,different affiliations may be attributed to predicting a particularpolitical affiliation, such as country music fans and NASCAR fans beingmore affiliated with the Republican Party. As a result, business logicrules may be used to implement these different affiliations into aprecondition that is met for retrieved secondary connections beforedeclaring 410 the attribute is exhibited by the first retrieved primaryconnection.

Steps 408 and 410 are repeated 412 for each of the remaining retrievedprimary connections of the first user in the seed cluster. Steps 406,408, 410 are repeated 414 for each of the remaining users in the seedcluster. A targeting cluster of users sharing the attribute is stored416 in the social networking system, where the targeting clustercomprises the seed cluster and the declared one or more primaryconnections. In one embodiment, where a connection of the declared oneor more primary connections is a particular user of the socialnetworking system, the targeting cluster of users includes identifyinginformation about that particular user. In another embodiment, where aconnection of the declared one or more primary connections is a non-userentity of the social networking system, the targeting cluster mayinclude identifying information of the non-user entity in the socialnetworking system such that users that interact with the non-user entitymay be identified as possible users to target.

SUMMARY

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a non-transitory, tangible computer readable storagemedium, or any type of media suitable for storing electronicinstructions, which may be coupled to a computer system bus.Furthermore, any computing systems referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

Embodiments of the invention may also relate to a product that isproduced by a computing process described herein. Such a product maycomprise information resulting from a computing process, where theinformation is stored on a non-transitory, tangible computer readablestorage medium and may include any embodiment of a computer programproduct or other data combination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A method comprising: determining a plurality ofseed users of an online social networking system, where each seed userpreviously self-declared a particular affiliation in the user's profileon the online social networking system; for each seed user: selecting aplurality of social connections connected to the seed user in the onlinesocial networking system, wherein each of the plurality of socialconnections has not previously self-declared the particular affiliation;and for each selected social connection of the plurality of socialconnections connected to the seed user in the online social networkingsystem, determining by a processor an inference that the socialconnection has the particular affiliation based on one or more secondaryconnections of the social connection having the particular affiliationsatisfying a predetermined criteria; and storing information defining atargeting cluster of users in the online social networking system, wherethe targeting cluster comprises the seed cluster of users and the one ormore social connections inferred to have the particular affiliation. 2.The method of claim 1, wherein determining a plurality of socialconnections of the seed user further comprises: retrieving a listing ofthe plurality of social connections connected to the seed user from theuser profile of the seed user; and determining the plurality of socialconnections connected to the seed user based on the retrieved listing.3. The method of claim 1, wherein determining a plurality of socialconnections of the seed user further comprises: retrieving a pluralityof edge objects associated the seed user in the online social networkingsystem; and determining the plurality of social connections connected tothe seed user based on the retrieved plurality of edge objects.
 4. Themethod of claim 1, wherein determining a plurality of social connectionsof the seed user further comprises: retrieving a plurality of contentobjects associated the seed user in the online social networking system;and determining the plurality of social connections connected to theseed user based on the retrieved plurality of content objects.
 5. Themethod of claim 1, wherein determining an inference that the socialconnection has the particular affiliation further comprises: responsiveto determining that a predetermined threshold percentage of one or moresecondary connections of the social connection that have the particularaffiliation, determining the inference that the social connection hasthe particular affiliation.
 6. The method of claim 1, whereindetermining an inference that the social connection has the particularaffiliation further comprises: responsive to a score exceeding apredetermined threshold score, the score determined using a scoringmodel on the one or more secondary connections of the social connectionthat have the particular affiliation, determining the inference that thesocial connection has the particular affiliation.
 7. The method of claim1, wherein the predetermined criteria comprises at least a predeterminedthreshold percentage of one or more secondary connections of the socialconnection having the particular affiliation.
 8. The method of claim 7,wherein the predetermined threshold percentage comprises at least eightypercent.
 9. The method of claim 1, further comprising: receiving aselection of the particular affiliation shared by the targeting clusterfrom an advertiser selecting targeting criteria for an ad request. 10.The method of claim 1, wherein inferring that the social connection hasthe particular affiliation based on one or more secondary connections ofthe social connection having the particular affiliation satisfying apredetermined criteria further comprises: determining a weighted averageusing a plurality of affinity scores of the seed user for the one ormore secondary connections as weights; and responsive to the determinedweighted average exceeding a predetermined threshold for the socialconnection, inferring that the social connection has the particularaffiliation.
 11. A non-transitory computer readable storage mediumstoring one or more programs configured to be executed by a serversystem, the one or more programs comprising instructions for:determining a plurality of seed users of an online social networkingsystem, where each seed user previously self-declared a particularaffiliation in the user's profile on the online social networkingsystem; for each seed user: selecting a plurality of social connectionsconnected to the seed user in the online social networking system,wherein each of the plurality of social connections has not previouslyself-declared the particular affiliation; and for each selected socialconnection of the plurality of social connections connected to the seeduser in the online social networking system, determining by a processoran inference that the social connection has the particular affiliationbased on one or more secondary connections of the social connectionhaving the particular affiliation satisfying a predetermined criteria;and storing information defining a targeting cluster of users in theonline social networking system, where the targeting cluster comprisesthe seed cluster of users and the one or more social connectionsinferred to have the particular affiliation.
 12. The non-transitorycomputer readable storage medium of claim 11, wherein determining aplurality of social connections of the seed user further comprises:retrieving a listing of the plurality of social connections connected tothe seed user from the user profile of the seed user; and determiningthe plurality of social connections connected to the seed user based onthe retrieved listing.
 13. The non-transitory computer readable storagemedium of claim 11, wherein determining a plurality of socialconnections of the seed user further comprises: retrieving a pluralityof edge objects associated the seed user in the online social networkingsystem; and determining the plurality of social connections connected tothe seed user based on the retrieved plurality of edge objects.
 14. Thenon-transitory computer readable storage medium of claim 11, whereindetermining a plurality of social connections of the seed user furthercomprises: retrieving a plurality of content objects associated the seeduser in the online social networking system; and determining theplurality of social connections connected to the seed user based on theretrieved plurality of content objects.
 15. The non-transitory computerreadable storage medium of claim 11, wherein determining an inferencethat the social connection has the particular affiliation furthercomprises: responsive to determining that a predetermined thresholdpercentage of one or more secondary connections of the social connectionthat have the particular affiliation, determining the inference that thesocial connection has the particular affiliation.
 16. The non-transitorycomputer readable storage medium of claim 11, wherein determining aninference that the social connection has the particular affiliationfurther comprises: responsive to a score exceeding a predeterminedthreshold score, the score determined using a scoring model on the oneor more secondary connections of the social connection that have theparticular affiliation, determining the inference that the socialconnection has the particular affiliation.
 17. The non-transitorycomputer readable storage medium of claim 11, wherein the predeterminedcriteria comprises at least a predetermined threshold percentage of oneor more secondary connections of the social connection having theparticular affiliation.
 18. The non-transitory computer readable storagemedium of claim 17, wherein the predetermined threshold percentagecomprises at least eighty percent.
 19. The non-transitory computerreadable storage medium of claim 11, further comprising: receiving aselection of the particular affiliation shared by the targeting clusterfrom an advertiser selecting targeting criteria for an ad request. 20.The non-transitory computer readable storage medium of claim 11, whereininferring that the social connection has the particular affiliationbased on one or more secondary connections of the social connectionhaving the particular affiliation satisfying a predetermined criteriafurther comprises: determining a weighted average using a plurality ofaffinity scores of the seed user for the one or more secondaryconnections as weights; and responsive to the determined weightedaverage exceeding a predetermined threshold for the social connection,inferring that the social connection has the particular affiliation.